No abstract

The self-sensing of compressive and flexural stress in cement-based materials (without embedded or attached sensors, without admixture requirement and without poling) by capacitance measurement has been demonstrated. The sensing involves two coplanar electrodes in the form of aluminum foil adhered to the cement-based material by using adhesive tape, which also serves as a dielectric film. The normal force associated with compressive or flexural loading is applied to the region between the electrodes. The stress regimes are the low-stress regime (up to 1500 Pa), the medium-stress regime (1500–7400 Pa) and the high-stress regime (7400–65 000 Pa). The lowest compressive/flexural stress demonstrated for effective sensing is 300 Pa. The fractional decrease in capacitance per unit stress is up to 2.2 × 10−6 Pa−1 and tends to decrease as the regime is changed from low to medium and to high-stress. The sensitivity tends to be greater for the compressive stress than flexural stress. However, when the normal stress (relevant to weighing) is considered, the sensitivity is higher for flexural loading than compressive loading. The strain contributes negligibly to the capacitance decrease, though the near-surface deformability of the cement-based material contributes. The change in capacitance upon stress application is attributed to the direct piezoelectric effect. The sensitivity is comparable for cement paste, mortar and concrete. Capacitance-based self-sensing is advantageous over resistance-based self-sensing in terms of the low-stress sensing effectiveness, absence of admixture or aggregate proportion requirements, essential absence of self-sensing effectiveness reduction by the presence of aggregates, wide applicability to existing concrete structures, and ease of applying electrodes.

Purpose The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and genetic hybrid algorithm (PGSA-GA), for solving structural optimization problems. Design/methodology/approach PGSA-GA is based on PGSA and three improved strategies, namely, elitist strategy of morphactin concentration calculation, strategy of intelligent variable step size and strategy of initial growth point selection based on GA. After a detailed formulation and explanation of its implementation, PGSA-GA is verified using the examples of typical truss and single-layer lattice shell. Findings Improved PGSA-GA was implemented and optimization was carried out for two typical optimization problems; then, a comparison was made between the PGSA-GA and other methods. The results show that the method proposed in the paper has the advantages of high efficiency and rapid convergence, which enable it to be used for the optimization of various types of steel structures. Originality/value Through the examples of typical truss and single-layer lattice shell, it shows that the optimization efficiency and effect of PGSA-GA are better than those of other algorithms and methods, such as GA, secondary optimization method, etc. The results show that PGSA-GA is quite suitable for structural optimization.

Purpose The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and particle swarm optimization hybrid algorithm (PGSA–PSO hybrid algorithm), for solving structural optimization problems. Design/methodology/approach To further enhance the optimization efficiency and precision of this algorithm, the optimization solution process of PGSA–PSO comprises two steps. First, an excellent initial growth point is selected by PSO. Then, the global optimal solution can be obtained quickly by PGSA and its improved strategy called growth space adjustment strategy. A typical mathematical example is provided to verify the capacity of the new hybrid algorithm to effectively improve the global search capability and search efficiency of PGSA. Moreover, PGSA–PSO is applied to the optimization design of a suspended dome structure. Findings Through typical mathematical example, the improved strategy can improve the optimization efficiency of PGSA considerably, and an initial growth point that falls near the global optimal solution can be obtained. Through the optimization of the pre-stress of a suspended dome structure, compared with other methods, the hybrid algorithm is effective and feasible in structural optimization. Originality/value Through the examples of suspended dome structure, it shows that the optimization efficiency and precision of PGSA–PSO are better than those of other algorithms and methods. PGSA–PSO is effective and feasible in structural optimization problems such as pre-stress optimization, size optimization, shape optimization and even topology optimization.

No abstract